Optoelectronics Letters, Volume. 21, Issue 6, 342(2025)
Sign language data quality improvement based on dual information streams
[1] [1] MIN Y, HAO A, CHAI X, et al. Visual alignment constraint for continuous sign language recognition[C]//2021 IEEE/CVF International Conference on Computer Vision, October 10-17, 2021, Montreal, QC, Canada. New York: IEEE, 2021: 11542-11551.
[2] [2] CHENG K L, YANG Z, CHEN Q, et al. Fully convolutional networks for continuous sign language recognition[C]//European Conference on Computer Vision Computer, August 23-28, 2020, Glasgow, UK. Heidelberg: Springer, 2020: 697-714.
[3] [3] NIU Z, MAK B. Stochastic fine-grained labeling of multi-state sign glosses for continuous sign language recognition[C]//European Conference on Computer Vision Computer, August 23-28, 2020, Glasgow, UK. Heidelberg: Springer, 2020: 172-186.
[4] [4] KOLLER O, FORSTER J, NEY H. Continuous sign language recognition: towards large vocabulary statistical recognition systems handling multiple signers[J]. Computer vision and image understanding, 2015, 141: 108-125.
[5] [5] ZHOU H, ZHOU W, QI W, et al. Improving sign language translation with monolingual data by sign back-translation[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 1316-1325.
[6] [6] ZHANG J, ZHOU W, XIE C, et al. Chinese sign language recognition with adaptive HMM[C]//2016 IEEE International Conference on Multimedia and Expo (ICME), July 11-15, 2016, Seattle, WA, USA. New York: IEEE, 2016: 1-6.
[7] [7] CHEN Y, ZUO R, WEI F, et al. Two-stream network for sign language recognition and translation[EB/OL]. (2022-11-02) [2023-12-12]. https://arxiv.org/abs/2211.01367.
[8] [8] CAMGOZ N C, HADFIELD S, KOLLER O, et al. Neural sign language translation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7784-7793.
[9] [9] ZHOU H, ZHOU W, ZHOU Y, et al. Spatial-temporal multi-cue network for sign language recognition and translation[J]. IEEE transactions on multimedia, 2021, 24: 768-779.
[10] [10] DENG J, DONG W, SOCHER R, et al. ImageNET: a large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 2009: 248-255.
[11] [11] GONG S, SHI Y, JAIN A. Low quality video face recognition: multi-mode aggregation recurrent network (MARN)[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), October 27-28, 2019, Seoul, Korea (South). New York: IEEE, 2019.
[12] [12] ITTNER D J, LEWIS D D, AHN D D. Text categorization of low quality images[C]//Symposium on Document Analysis and Information Retrieval, 1995, Las Vegas, NV, USA. ISRI: University of Nevada, 1995: 301-315.
[13] [13] FEICHTENHOFER C, FAN H, MALIK J, et al. SlowFast networks for video recognition[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019: 6202-6211.
[14] [14] WANG W, ZHENG V W, YU H, et al. A survey of zero-shot learning: settings, methods, and applications[J]. ACM transactions on intelligent systems and technology (TIST), 2019, 10(2): 1-37.
[15] [15] YU J, GAO H, CHEN Y, et al. Adaptive spatiotemporal representation learning for skeleton-based human action recognition[J]. IEEE transactions on cognitive and developmental systems, 2022, 14(4): 1654-1665.
[16] [16] HE X, DENG K, WANG X, et al. LightGCN: simplifying and powering graph convolution network for recommendation[C]//Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, July 25-30, 2020, Virtual. New York: ACM, 2020: 639-648.
[17] [17] WU F, SOUZA A, ZHANG T, et al. Simplifying graph convolutional networks[EB/OL]. (2019-02-19)[2023-12-12]. https://arxiv.org/abs/1902.07153.
[18] [18] GENG X, LI Y, WANG L, et al. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting[J]. Proceedings of the AAAI conference on artificial intelligence, 2019, 33(1): 3656-3663.
[19] [19] YAN S, XIONG Y, LIN D. Spatial temporal graph convolutional networks for skeleton-based action recognition[J]. Proceedings of the AAAI conference on artificial intelligence, 2018, 32(1): 6665-7655.
[20] [20] LAMPERT C H, NICKISCH H, HARMELING S. Learning to detect unseen object classes by between-class attribute transfer[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA. New York: IEEE, 2009: 951-958.
[21] [21] CHEN Z, HUANG Y, CHEN J, et al. Duet: cross-modal semantic grounding for contrastive zero-shot learning[EB/OL]. (2022-07-04) [2023-12-12]. https://arxiv.org/abs/2207.01328.
[22] [22] YANG Z, LI K, GAN H, et al. HD-GCN: a hybrid diffusion graph convolutional network[EB/OL]. (2023-03-31) [2023-12-12]. https://arxiv.org/abs/2303.17966.
[23] [23] CHEN Y, ZHANG Z, YUAN C, et al. Channel-wise topology refinement graph convolution for skeleton-based action recognition[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV), October 10-17, 2021, Montreal, QC, Canada. New York: IEEE, 2021, 13359-13368.
[24] [24] YANG C, XU Y, SHI J, et al. Temporal pyramid network for action recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 14-19, 2020, Seattle, WA, USA. New York: IEEE, 2020: 591-600.
[25] [25] LI J, HAN B, JIANG M. Anomaly monitoring and early warning of electric moped charging device with infrared image[J]. Optoelectronics letters, 2025, 21(3): 136-141.
Get Citation
Copy Citation Text
CAI Jialiang, YUAN Tiantian. Sign language data quality improvement based on dual information streams[J]. Optoelectronics Letters, 2025, 21(6): 342
Received: Jul. 18, 2023
Accepted: Jun. 27, 2025
Published Online: Jun. 27, 2025
The Author Email: